Eye Docking Alignment Using Predicted Corneal Surface Position
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Solution Overview
Problem
Existing ophthalmic laser systems face challenges in precisely docking the patient interface to the eye, especially when patients cannot reliably fixate or the eye shifts during the docking process, leading to misalignment and potential inaccuracies in laser treatment.
Innovation Solution
An ophthalmic laser surgical system that uses cameras and a computer to determine the predicted and actual positions of anatomical features of the eye, comparing them to detect misalignment and adjust the docking process, utilizing illuminators for reference reflections and a patient interface to ensure precise alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If a patient interface is affixed to the eye to limit movement and create a reliable optical interface, then the stability of the docking is improved, but the complexity of the docking process increases due to the need for precise alignment detection and adjustment
Solution Approach 1:
The system performs preliminary actions by capturing images of the eye before docking occurs and using these images to determine the predicted corneal surface position. This advance preparation allows the system to compare predicted versus actual positions and detect misalignment before the laser treatment begins, thereby improving docking stability without requiring complex real-time adjustment mechanisms during the actual treatment.
Solution Approach 2:
The system implements feedback by comparing the predicted corneal surface position (derived from pre-docking images and eye models) with the actual corneal surface position detected from post-docking images. This feedback loop enables the system to detect misalignment and adjust the docking process, improving stability while managing complexity through automated comparison and adjustment protocols.
2Measurement precision
If the eye is subjected to various conditions (different eye axes, light intensities, contact with patient interface) to gather comprehensive data, then the accuracy of predicted anatomical features is improved, but the time required for data collection and processing increases
Solution Approach 1:
The system applies parameter changes by capturing eye images under varying conditions (different eye axes, light intensities, contact with patient interface) and using these varied parameters to gather comprehensive data about the eye's anatomical features. This allows the system to improve prediction accuracy by analyzing how the eye appears under multiple conditions, thereby justifying the time investment through more reliable alignment data.
Solution Approach 2:
The system performs preliminary data collection by capturing images under various conditions before the actual docking and treatment process. This advance data gathering allows the system to pre-calculate predicted anatomical features and compare them with actual positions during docking, improving accuracy without requiring time-consuming real-time analysis during the critical treatment window.
3Reliability
If the system compares predicted and actual corneal surface positions to detect misalignment, then the reliability of laser treatment is improved, but the computational complexity and processing time increase
Solution Approach 1:
The system performs preliminary computational work by calculating the predicted corneal surface position before docking using pre-docking images and eye models. This advance computation allows the system to prepare alignment expectations in advance, so that during the actual docking process, the system only needs to compare predicted versus actual positions rather than performing complex real-time calculations, thereby improving reliability while managing computational complexity.
Solution Approach 2:
The system implements feedback by comparing predicted corneal surface positions with actual positions detected from post-docking images. This feedback mechanism provides a straightforward computational task (comparison rather than complex calculation) that reliably detects misalignment and ensures treatment accuracy, improving reliability without proportionally increasing computational complexity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system enables accurate and reliable docking of the patient interface by detecting and compensating for misalignment, ensuring precise delivery of laser pulses to targeted eye locations, thereby improving the accuracy of ophthalmic laser treatments.
Implementation Method 1
The ophthalmic laser surgical system includes illuminators that illuminate the eye to yield reference reflections
Data Source
AI summary
An ophthalmic laser surgical system for treating an eye includes a laser device, one or more cameras, and a computer. The eye has anatomical features, including the cornea with the anterior corneal surface. The laser device directs a laser beam towards the eye. A camera generates images of the anatomical features, including the anterior corneal surface. The computer facilitates docking a patient interface onto the eye by accessing eye information describing the eye. The eye information comprises an eye model describing the anatomical features. The computer determines from the eye model the predicted corneal surface position when the eye is aligned to dock the patient interface onto the eye. The computer detects from the images the actual corneal surface position prior to docking the patient interface onto the eye, and compares the predicted corneal surface position with the actual corneal surface position to detect misalignment.